Method and apparatus for tracking musical score

ABSTRACT

A score tracking method and apparatus are provided. The score tracking method includes a first step of detecting a list of frequency models, which are expected to occur during performance of a score, from score information; a second step of generating a matching model, which is composed of frequency components that are expected to be included in audio data externally input at the time point, based on the frequency model list; a third step of receiving external audio data, converting the audio data into digital signal, and detecting current performance frequency data from the digital signal; a fourth step of determining whether a new frequency component is included in the current performance frequency data; a fifth step of determining whether the matching model matches the current performance frequency data when a new frequency component is included in the current performance frequency data; and a sixth step of generating synchronized information between the current performing notes and score notes updating the matching model matches the current performance frequency data in the fifth step. Through those steps, a performance location in the score is automatically determined, and the result of determination is provided to a user.

TECHNICAL FIELD

The present invention relates to a score tracking method and apparatusfor automatically tracking a performance location in a score.

Generally, people use scores when practicing or playing a musicalinstrument such as a piano or violin. In other words, except a specialcase such as a test, people refer to a score when practicing or playingmusic. For example, students refer to what a teacher said that waswritten down on a score when studying music, and players of a symphonyorchestra write down interpretation instructed by a conductor on a scoreand refer to it when playing.

Usually, players use both hands when playing musical instruments.Accordingly, the players need someone to help to leaf through a score orneed to quickly turn a page of a score with one hand by themselvesduring performance. Therefore, the players cannot be devoted to onlyperformance.

BACKGROUND ART

In order to solve the problem, conventionally, an additional apparatussuch as a timer or pedal is installed at a music stand, which is usedfor holding a score, so that players can leaf through the score withoutusing a hand, or methods for automatically leafing through a score havebeen proposed. However, according to these methods, it is difficult toappropriately set a time, at which a page of a score is turned, due todifference in performance tempo among players.

In another method, pitch information of monophonic note, which iscurrently performed, is detected and matched with note on a score sothat a time at which a page of the score is turned is determined basedon real performance information. However, this method can be appliedwhen only monophonic notes are performed but cannot be applied whenpolyphonic notes are performed using, for example, a violin, a guitar,and a piano or when a concerto is performed.

DISCLOSURE OF THE INVENTION

To overcome the above problems, it is a first object of the presentinvention to provide a score tracking method and apparatus foraccurately tracking a performance location in a score when polyphonicnotes or a concerto as well as monophonic note is performed, by matchingfrequency models, which are expected from score information, withfrequency components of externally input sounds.

It is a second object of the present invention to provide a recordingmedium for recording control commands for executing the above scoretracking method.

It is a third object of the present invention to provide acomputer-executable method for automatically tracking a performancelocation in a score according to the above score tracking method.

To achieve the first object of the present invention, there is provideda score tracking method including a first step of detecting a list offrequency models, which are expected to occur during performance of ascore, from score information; a second step of generating a matchingmodel, which is composed of frequency components that are expected to beincluded in audio data externally input at the time, based on thefrequency model list; a third step of receiving external audio data,converting the audio data into digital signal, and detecting currentperformance frequency data from the digital signal; a fourth step ofdetermining whether a new frequency component is included in the currentperformance frequency data; a fifth step of determining whether thematching model matches the current performance frequency data when a newfrequency component is included in the current performance frequencydata; and a sixth step of generating synchronized information betweenthe real performing notes and the score notes and updating the matchingmodel, when it is determined that the matching model matches the currentperformance frequency data in the fifth step.

There is also provided a score tracking apparatus including a digitalsignal input unit that receives music performed outside and converts itinto digital signal; a frequency analyzer that extracts frequencycomponents from the digital signal; a score information input unit thatinputs score information, which comprises pitch and length informationof each note included in a score to be performed; a frequency modelingunit that detects frequency models of notes, which are to besimultaneously performed at each time point when a new note is expectedto be performed, by performing frequency modeling on the scoreinformation, and generates a list of the frequency models; a is storageunit that stores and manages the generated frequency model list; aperformance location determiner that receives a frequency component ofcurrently performed digital signal from the frequency analyzer andperforms a matching operation on the received frequency component andthe frequency model list stored in the storage unit so as to determine acurrent performance location; and a determination result output unitthat provides the result of the performance location determiner to auser.

To achieve the second object of the present invention, there is provideda computer-readable recording medium in which control commands forexecuting the above score tracking method is recorded so that the scoretracking method can be executed when the computer-readable recordingmedium is driven by a computer.

To achieve the third object of the present invention, there is provideda computer-executable method for automatically tracking a performancelocation in a score. The computer-executable method includes a firststep of receiving an execution command; a second step of detecting alist of frequency models, which are expected to occur during musicperformance, from information about a score to be performed, in responseto the execution command; a third step of analyzing a frequencycomponent of digital signal, which is performed outside and receivedthrough an audio input unit of the computer or which is reproduced inthe computer; a fourth step of generating a matching model of afrequency component, which is expected to occur at the time during themusic performance, based on the frequency model list detected in thesecond step; and a fifth step of comparing the frequency component thatis analyzed in the third step with the matching model generated in thefourth step to track a current performance location.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a score tracking apparatus according toan embodiment of the present invention.

FIG. 2 is a flowchart of a score tracking method according to anembodiment of the present invention.

FIG. 3 is a flowchart of a procedure for detecting an extractedfrequency model (XFM) list according to the embodiment of the presentinvention.

FIG. 4 is a flowchart of a procedure for generating a matching modelaccording to the embodiment of the present invention.

FIG. 5 is a flowchart of a procedure for inputting digital signalaccording to the embodiment of the present invention.

FIG. 6 is a flowchart of a procedure for determining onset/non-onset ofa new frequency according to the embodiment of the present invention.

FIG. 7 is a flowchart of a procedure for updating the matching modelaccording to the embodiment of the present invention.

FIG. 8 is a flowchart of a procedure for adding a matching modelaccording to the embodiment of the present invention.

FIGS. 9A through 9C show an example of a procedure for detecting the XFMlist from score information according to the embodiment of the presentinvention.

FIG. 10 shows an example of a procedure for generating and updating thematching model according to the embodiment of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

Before setting forth a score tracking method and apparatus according tothe present invention, terms used for explaining preferred embodimentsof the present invention will be briefly reviewed.

An extracted frequency model (XFM) indicates a model of a frequency,which is detected from score information and is expected to be generatedwhen music is performed with reference to the score.

A passed notes frequency model (PNFM) indicates a frequency model of anote, which was performed before the time. When it is determined whetherreal performance information matches the score information, the PNFM isused to consider frequency data of sound which still remains at presenteven if the sound was generated for a passed note.

A current notes frequency model (CNFM) indicates a frequency model of anote that is currently performed.

An expected notes frequency model (ENFM) indicates a frequency model ofa note that is expected to be performed next.

A matching model M indicates an object which is compared with the realperformance information at the time when it is determined whether thereal performance information matches the score information. The matchingmodel M is generated by combining the PNFM, the CNFM, and the ENFM.

A current performance frequency data (CPFD) indicates frequencycomponents of digital signal that is currently input.

A previous performance frequency data (PPFD) indicates a CPFD inputtedpreviously.

A score time (S_Time) indicates a value which is allocated to an XFMdetected from the score information. The S_Time means a standby timefrom a time point when a current XFM is performed to a time point whenthe next XFM is expected to be performed and is used to detect amatching time (M_Time).

A tempo ratio (T_Ratio) indicates a ratio of a real performance time tothe S_Time and is used to detect the M_Time.

Tempo variance (T_Variance) indicates variance of the T_Ratio and isused to detect the M_Time.

The M_Time indicates a standby time from a time point when matchingoccurs to a time point when the next matching is expected to occur andis detected from the S_Time, the T_Ratio, and the T_Variance.

A performance time (P_Time) indicates a real time that lapses sincematching occurs, that is, a duration during which performance of acurrent note has been continued.

Hereinafter, a score tracking method and apparatus of the presentinvention will be described in detail with reference to the attacheddrawings.

FIG. 1 is a schematic diagram of a score tracking apparatus according toan embodiment of the present invention. Referring to FIG. 1, the scoretracking apparatus of the present invention includes a digital signalinput unit 10, a frequency analyzer 20, a score information input unit30, a frequency modeling unit 40, a storage unit 50, a performancelocation determiner 60, and a determination result output unit 70.

The digital signal input unit 10 converts externally performed musicinto digital signal and receives the digital signal. When music is inputthrough a microphone, the digital signal input unit 10 converts themusic into digital signal using an analog-to-digital (A/D) converter.When music is input in the form of a music file such as music on a CD,MP3, or wave file, the digital signal input unit 10 converts the musicinto a wave format. Generally, in case of a computer, a sound cardfunctions as an A/D converter.

The frequency analyzer 20 extracts a CPFD, which is performed currently,from the digital signal that is input from the digital signal input unit10. Here, Fast Fourier Transformation (FFT) is used to analyze thedigital signal into frequency. However, another method such as wavelettransformation may be used.

The score information input unit 30 receives score information of notesthat is to be performed. More specifically, the score information inputunit 30 inputs digital score data including pitch information and lengthinformation of each note. For example, the pitch and length informationof each note can be extracted through scanning the score image orreading the score file written by any score writing software.

The frequency modeling unit 40 detects an XFM list by performingfrequency modeling on the score information that is input from the scoreinformation input unit 30. In other words, the frequency modeling unit40 detects XFMs of notes, which are to be simultaneously performed at atime point when a new note is expected to be performed, based on thescore information and generates a list of the XFMs. A procedure forgenerating the XFM list from the score information will be described indetail with reference to FIGS. 9A through 9C.

The storage unit 50 stores and manages the XFM list that is generated bythe frequency modeling unit 40.

When the performance location determiner 60 receives the CDFD of thecurrently performed digital signal from the frequency analyzer 20, itperforms a matching operation on the CPFD and the XFM list stored in thestorage unit 50 to determine a current performance location.

More specifically, the performance location determiner 60 generates amatching model M of a frequency component, which is expected to begenerated at the time during performance of the music, based on the XFMlist stored in the storage unit 50 and performance a matching operationon the CPFD received from the frequency analyzer 20 and the matchingmodel M. Then, the performance location determiner 60 determines thecurrent performance location based on the result of the matchingoperation.

Here, the matching model M is constituted by combining a PNFM, a CNFM,and an ENFM with respect to the time. The PNFM, the CNFM, and the ENFMare extracted from the XFM list.

The determination result output unit 70 provides the determinationresult received from the performance location unit 60. Here, a varietyof methods including directly indicating a relevant performance locationin an electronic score can be used to provide the determination resultto the user. Due to determination of the current performance location,the distance between the current performance location and the lastlocation of a current page of the score can be determined so that asignal for turning the page can be generated at an appropriate timepoint.

FIG. 2 is a flowchart of a score tracking method according to anembodiment of the present invention. Referring to FIG. 2, a list ofXFMs, which are expected to match with input frequency data when musicis performed with reference to the score, is detected from scoreinformation in step S110. Here, if audio information of a musicalinstrument to be played is obtained in advance, the XFM list is detectedfrom the audio information. Otherwise, the XFM list is detected based onharmonic frequency of each note according to the kind of musicalinstrument.

Based on the XFM list, a matching model M, which is composed offrequency components that are expected to be included in the audio dataexternally input at the time, is generated in step S120.

Here, the matching model M is obtained by combining a PNFM which is afrequency model of a note performed before a current time, a CNFM whichis a frequency model of a note performed at the current time, and anENFM which is a frequency model of a note that is expected to beperformed at the next time point, as shown in Formula (1).M=PNFM+CNFM+ENFM  (1)

If the audio data is externally input, the audio data is converted intodigital signal in step S130, and a CPFD is detected from the digitalsignal in step S140. Here, FFT is usually used to detect the CPFD fromthe digital signal, but another method such as wavelet transformationmay be used.

It is determined whether a new frequency component is included in theCPFD, that is, it is determined onset/non-onset of a new frequencycomponent, in steps S150 and S160. If it is determined that a newfrequency component is included, that is, onset is determined, the CPFDis compared with the matching model M in step S170. More specifically,it is determined whether all frequency components included in thematching model M are included in the CPFD. Here, when there are aplurality of matching models M, an initial matching model M (forexample, a matching model M which was generated at the earliest timepoint) is first selected and compared with the CPFD.

As a result of performing step S170, if it is determined that thematching model M matches the CPFD in step S180, a synchronizedinformation between the real performing notes and the score notes isgenerated in step S190, and the matching model M is updated in stepS200. When updating the matching model M or adding another matchingmodel M, a M_Time used for score tracking is also set. The M_Time is seton the basis of a S_Time, which is an expected standby time from a timepoint when the matching model M matches the CPFD to a time point whenthe ENFM of the matching model M is to be performed. More specifically,the M_Time is calculated by multiplying the S_Time by a T_Ratio and aT_Variance to consider the performance tempo of a relevant player, asshown in Formula (2).M _(—) Time=S _(—) Time*T _(—) Ratio*T _(—) Variance  (2)

On the contrary, as a result of performing step S170, if it isdetermined that the matching model M does not match the CPFD, theremaining matching models that have not been matched after beinggenerated are sequentially selected starting from the earliest one andcompared with the CPFD in step S210 and S220. If there is a matchingmodel M that match the CPFD as a result of comparison, a synchronizedinformation between the real performing notes and the score notes isgenerated in step S190, and the remaining matching models M other thanthe matching model M matching the CPFD are removed, and the matchingmodel M is updated in step S200.

If matching between the CPFD and the matching model M is not achieveduntil a P_Time, during which a current performing note included in theCPFD is continued, is equal to or greater than the M_Time which is setfor the matching model that is generated to be compared with the CPFD instep S230, a new matching model is added in step S240. In other words,the new matching model including the XFM, which is expected to beperformed at the next time point following the ENFM included in thecurrent matching model, is added in step S240. This is for compensatingfor a case where a player omits a note on the score.

FIG. 3 is a flowchart of the step S10 of detecting the XFM listaccording to the embodiment of the present invention. Referring to FIG.3, information about the score to be performed is read in step S111.Then, it is checked whether there is audio information of a musicalinstrument to be played in step S112.

If it is determined that there is audio information of a relevantmusical instrument in step S112, audio information of each note includedin the score information is brought from the audio information in stepS113, and the XFM list is detected using peak values among frequencycomponents of the audio information of each note in step S114.

If it is determined that there is no audio information of a relevantmusical instrument in step S112, harmonic frequency components of eachnote corresponding to each type of musical instrument are extracted instep S115. Then, the XFM list is detected using peak values among theharmonic frequency components in step S116.

FIG. 4 is a flowchart of the step S120 of generating the matching modelM according to the embodiment of the present invention.

Referring to FIG. 4, for the initially matching model M, sinceperformance is not started yet, the PNFM and the CNFM that constitutethe matching model M are set to be null in step S121, and the ENFM isset to an XFM, which is expected to be initially performed from the XFMlist, in step S122. The PNFM, the CNFM, and the ENFM are combined togenerate the matching model M in step S123.

In addition, in order to adjust a score tracking speed in accordancewith a performance tempo depending on the characteristics of the playeror a reproducing apparatus, the initial values of variables, i.e.,M_Time, P_Time, T_Ratio, and T_Variance, which are used for determiningthe performance tempo, are set in step S124.

The M_Time indicates a standby time of each matching model for matching.For the initial matching model M, since the performance is not startedyet, the M_Time is set to an invalid arbitrary value (form example,NULL).

The P_Time indicates a continuous time of a current performing note. Theinitial value of the P_Time is set to “0”, and the value is increaseduntil the matching model M matches the CPFD.

The T_Ratio indicates a performance tempo ratio, that is, a ratio ofreal matching time to the M_Time and has an initial value of 1. Forexample, when T_Ratio is 2, real performance is two times slower than isexpected.

The T_Variance indicates performance tempo variance and has an initialvalue of 1. For example, when T_Variance is 1.1, the tempo of the realperformance decreases 10 percents.

FIG. 5 is a flowchart of the step S130 of inputting digital signalaccording to the embodiment of the present invention. Referring to FIG.5, input of the digital signal is continued until an end command isinput from the outside in step S131. Input of the digital signal varieswith a method of inputting external audio data. More specifically, insteps S132 through S134, when the audio data is input through amicrophone, A/D conversion is performed S133, and when the audio data isinput in the form of a music file, the format of the music file isconverted S134.

The converted audio information is input in units of frames in stepS135. In order to determine the performance tempo, the P_Time indicatingtime during which a current performing note is continued is increased instep S136.

FIG. 6 is a flowchart of the step S150 of determining onset/non-onset ofa new frequency according to the embodiment of the present invention.Referring to FIG. 6, a frequency component that disappears at a currenttime is detected and is removed from frequency information included inthe matching model M in step S151. For this, the CPFD included in thecurrently input audio data is compared with the PPFD included in audiodata that was input at the previous time point. Then, a frequencycomponent that is included in the PPFD but is not included in the CPFDis detected and removed.

Next, in steps S152 through S155, strengths and frequency componentvalues are compared between the CPFD and the PPFD to determine whether anew frequency component is included in the CPFD, that is, to determineonset/non-onset of a new frequency component. Here, while detectingonset in order to determine whether the next note has been performed,onset is determined based on a rapid increase in the strength of audioin steps S152 and S153 and is then determined based on whether a newfrequency component is included in the CPFD in order to take intoaccount a case where audio, such as sound of a wind instrument, changesonly in pitch without having a change in strength in steps S154 andS155.

As a result of determination performed in steps S152 through S155, ifonset is determined, an onset flag is set in step S158. Otherwise, anon-onset flag is set in step S157.

In the meantime, if it is determined that the P_Time exceeds the M_Timefor the current matching model M in step S156, it is considered thatonset occurs and thus an onset flag is set in step S158.

FIG. 7 is a flowchart of the step S200 of updating the matching model Maccording to the embodiment of the present invention. More specifically,FIG. 7 is a flowchart of a procedure for updating a matching modelM₀=PNFM₀+CNFM₀+ENFM₀ having a score time S_Time₀ with a matching modelM₁=PNFM₁+CNFM₁+ENFM₁ having a score time S_Time₁.

Referring to FIG. 7, it is determined whether the XFM list is empty stepS201. The update of the matching model proceeds as follows when it isdetermined that the XFM list is not empty.

In other words, the matching model M₁ is generated by performingfrequency transfer on the matching model M₀. This will be described inmore detail below.

In step S202, a new passed notes frequency model PNFM₁ is generated bycombining a passed notes frequency model PNFM₀ and a current notesfrequency model CNFM₀ included in the matching model M₀, that is,PNFM₁←PNFM₀+CNFM₀; a new current notes frequency model CNFM₁ isgenerated using an expected notes frequency model ENFM₀ included in thematching model M₀, that is, CNFM₁←ENFM₀; and a new expected notesfrequency model ENFM₁ is generated by selecting an extracted frequencymodel XFM₁, which is expected to be performed at the next time point,from the XFM list.

Then, in step S203, a new matching model M₁ is generated by combiningthe frequency models, i.e., PNFM₁, CNFM₁, and ENFM₁.

If the new matching model M₁ is generated with such arrangement, amatching time M_Time, for the matching model M₁ is detected in stepsS204 through S209.

More specifically, in step S204, the value of the T_Ratio and the valueof the T_Variance are calculated with respect to current performance instep S204. In step S209, the matching time M_Time, for the new matchingmodel M₁ is calculated using the T_Ratio and the T_Variance, and sincematching of the matching model M₀ is accomplished, the P_Time isinitialized.

In the meantime, in order to prevent the M_Time from rapidly andincorrectly changing due to a mistake or wrong matching, in steps S205through S208 the limits of the T_Variance are fixed to a minimum valueMIN and a maximum value MAX so that the T_Variance is set within thelimits.

FIG. 8 is a flowchart of the step S240 of adding another matching modelM according to the embodiment of the present invention. Morespecifically, FIG. 8 shows a procedure of adding a new matching modelM₁=PNFM₁+CNFM₁+ENFM₁ having a score time S_Time₁ with the existingmatching model M₀=PNFM₀+CNFM₀+ENFM₀ having a score time S_Time₀ left asit is when the matching model M₀ does not have any match until amatching time M_Time₀ for the matching model M₀ lapses.

Referring to FIG. 8, the adding of the matching model proceeds asfollows when it is determined that the XFM list is not empty in stepS241.

In step S243, a passed notes frequency model PNFM₀ and a current notesfrequency model CNFM₀ included in the matching model M₀ are taken asthey are; and only a new expected notes frequency model ENFM₁ isgenerated as an extracted frequency model XFM₁, which is expected to beperformed at the next time point after performance of an expected notesfrequency model ENFM₀. In other words, the passed notes frequency modelPNFM₀ and the current notes frequency model CNFM₀ are applied as theyare as new passed notes frequency model PNFM, and current notesfrequency model CNFM₁, respectively, and only the new expected notesfrequency model ENFM, is set to the newly selected frequency model XFM₁.

Then, in step S244, a new matching model M₁ is generated by combiningthe frequency models, i.e., PNFM₁, CNFM₁, and ENFM₁.

If the new matching model M₁ is generated with such arrangement, amatching time M_Time₁, during which the expected notes frequency modelENFM, included in the matching model M₁ is expected to be performed, iscalculated in steps S245 and S246. Here, since there is no variance intempo, a new matching time M_Time, for the new matching model M₁ iscalculated using tempo information, i.e., T_Ratio₀, of the matchingmodel M₀, as shown in step S246 of FIG. 8.

In the meantime, when errors consecutively occur in performing musicduring a predetermined period of time, it is meaningless to continuescore tracking. Accordingly, the number of matching models M, whichremain without having matches after being generated, is limited, and ifthe number of remaining matching models M exceeds a predetermined limitvalue in step S242, a new matching model is not added and the operationends.

FIGS. 9A through 9C show an example of a procedure for detecting the XFMlist from score information according to the embodiment of the presentinvention.

When score information shown in FIG. 9A is input to the frequencymodeling unit 40 shown in FIG. 1, the frequency modeling unit 40receives pitch and length information of each note of the scoreinformation, as shown in FIG. 9B, and detects frequency models usingpitch information of the individual notes, as shown in FIG. 9C.

Here, each of time intervals A, B, C, D, and E shown in FIG. 9B is setfor a note to be performed in a corresponding time interval A, B, C, D,or E and for waiting for input of sound of the next note. For example,during the time interval A, a ¼ D5 note, a ½ B3 note, and a ½ G3 noteare simultaneously performed, and performance of a ⅛ G4 note is waitedfor. In addition, the length of each time interval A, B, C, D, or E isusually determined based on the length of a note which is performed onlyin the corresponding time interval A, B, C, D, or E. For example, whenthe length of the time interval A is set to “t”, each of the remainingtime intervals B, C, D, and E is set to “½t”.

FIG. 9C shows frequency models detected based on the pitch and lengthinformation of the individual notes included in the score information.FIG. 9C(a) shows frequency models of notes included in the time intervalA. FIG. 9C(b) shows a frequency model of a note included in the timeinterval B. FIG. 9C(c) shows a frequency model of a note included in thetime interval C. FIG. 9C(d) shows frequency models of notes included inthe time interval D. FIG. 9C(e) shows a frequency model of a noteincluded in the time interval E. When a plurality pieces of noteinformation is included in a single time interval as shown in FIGS.9C(a) and (d), a value of summation of all frequency models included ineach time interval is stored in the storage unit 50.

FIG. 10 shows an example of a procedure for generating and updating amatching model according to the embodiment of the present invention.

FIGS. 10(a), 10(d), and 10(g) show PNFMs at the times, respectively.FIGS. 10(b), 10(e), and 10(h) show CNFMs at the respective the times.FIGS. 10(c), 10(f), and 10(i) show ENFMs at the respective the timespoints.

FIGS. 10(a), 10(b), and 10(c) show frequency models which constitute aninitially set matching model M₀. FIGS. 10(d), 10(e), and 10(f) showfrequency models which constitute a matching model M₁, which is newlygenerated through frequency transfer after the matching model M₀ wasmatched with a CPFD at the time. FIGS. 10(g), 10(h), and 10(i) showfrequency models which constitute a matching model M₂, which is newlygenerated through frequency transfer after the matching model M₁ wasmatched with a CPFD at a time point after the above the time.

According to the present invention, control commands for executing theabove-described steps can be recorded in a computer-readable recordingmedium so that the steps can be performed in a general computer.

The above description just concerns embodiments of the presentinvention. The present invention is not restricted to the aboveembodiments, and various modifications can be made thereto within thescope defined by the attached claims. For example, the shape andstructure of each member specified in the embodiments can be changed.

Industrial Applicability

According to a score tracking method and apparatus of the presentinvention, a performance location in a score can be automaticallytracked based performance information on monophonic or polyphonic notesperformed in real time. A page-turning signal is automatically generatedat an appropriate time point, based on the result of tracking theperformance location, so that players do not need to turn pages of ascore himself/herself using a timer or a special switch. Therefore,players can be devoted to performance only.

1. A score tracking method comprising: a first step of detecting a list of frequency models, which are expected to occur during performance of a score, from score information; a second step of generating a matching model, which is composed of frequency components that are expected to be included in audio data externally input at the time, based on the frequency model list; a third step of receiving external audio data, converting the audio data into digital signal, and detecting current performance frequency data from the digital signal; a fourth step of determining whether a new frequency component is included in the current performance frequency data; a fifth step of determining whether the matching model matches the current performance frequency data when a new frequency component is included in the current performance frequency data; and a sixth step of generating synchronized information between the real performing notes and the score notes and updating the matching model, when it is determined that the matching model matches the current performance frequency data in the fifth step.
 2. The score tracking method of claim 1, wherein the first step comprises: (1-1) reading information of the score to be performed; (1-2) determining whether there is audio information about musical instruments to be played according to the score; (1-3) when it is determined that there is audio information about musical instruments, extracting audio information of each note, which is included in the score information, from the audio information about musical instruments and detecting the frequency model list using peak values among frequency components of the audio information of each note; and (1-4) when it is determined that audio information about musical instruments does not exist, extracting harmonic frequency components of each note, which corresponds to each type of musical instrument and is included in the score information, and detecting the frequency model list using peak values among the harmonic frequency components.
 3. The score tracking method of claim 1, wherein the second step comprises generating the matching model by combining a passed notes frequency model, which is a frequency model of a note that has been performed previously, a current notes frequency model, which is a frequency model of a note that is performed at the current time, and an expected notes frequency model, which is a frequency model of a note that is expected to be performed next.
 4. The score tracking method of claim 3, wherein the second step comprises the steps of: (2-1) setting the passed notes frequency model and the current notes frequency model to a null value, selecting a frequency model, which is expected to be initially performed, from the frequency model list, and setting the frequency model as the expected notes frequency model; (2-2) generating the matching model by combining the passed notes frequency model, the current notes frequency model, and the expected notes frequency model; and (2-3) setting a matching time for the matching model to an invalid value.
 5. The score tracking method of claim 1, wherein the third step comprises performing analog-to-digital conversion when the external audio data is received through a microphone and converting the format of a music file when the external audio data is received in the form of the music file so that the external audio data is converted into the digital signal.
 6. The score tracking method of claim 1, wherein the fourth step comprises the steps of: (4-1) comparing the current performance frequency data, which is included in the audio data input at the current time, with previous performance frequency data, which was included in audio data inputted previously, and detecting a frequency component disappearing at the current time; (4-2) removing the disappearing frequency component from frequency components included in the matching model; (4-3) comparing the current performance frequency data with the previous performance frequency data in both strength and frequency component value so as to determine whether a new frequency component is included in the current performance frequency data; and (4-4) determining that a new frequency component is included in the current performance frequency data either when it is determined that a new frequency component is included in the current performance frequency data in step (4-3) or when a matching time for the matching model has lapsed since previous matching was accomplished in a state where a new frequency component does not exist.
 7. The score tracking method of claim 1, wherein the sixth step comprises the steps of: (6-1) generating a new passed notes frequency model by combining a passed notes frequency model and a current notes frequency model which are included in the matching model; (6-2) generating a new current notes frequency model using an expected notes frequency model included in the matching model; (6-3) selecting a frequency model, which is expected to be performed next, from the frequency model list and generating it as a new expected notes frequency model; (6-4) generating a new matching model by combining the new passed notes frequency model, the new current notes frequency model, and the new expected notes frequency model; and (6-5) determining a current performance tempo by comparing the matching time for the matching model with time information from a time point when the matching model is generated to a time point when a real matching is accomplished and detecting a matching time for the new matching model based on the current performance tempo.
 8. The score tracking method of claim 1, when the matching model does not match the current performance frequency data in the fifth step, further comprising: a seventh step of sequentially selecting matching models, which remain without being matched since being generated, starting from a matching model that was generated first and determining whether they match the current performance frequency data; and an eighth step of when a selected matching model matches the current performance frequency data, generating synchronized information between the real performing notes and the score notes, removing the remaining matching models other than the selected matching model, and updating the matching model.
 9. The score tracking method of claim 8, wherein the eighth step comprises the steps of: (8-1) generating a new passed notes frequency model by combining a passed notes frequency model and a current notes frequency model, which are included in the matching model; (8-2) generating a new current notes frequency model using an expected notes frequency model included in the matching model; (8-3) selecting a frequency model, which is expected to be performed next, from the frequency model list and generating it as a new expected notes frequency model; (8-4) generating a new matching model by combining the new passed notes frequency model, the new current notes frequency model, and the new expected notes frequency model; and (8-5) determining a current performance tempo by comparing the matching time for the matching model with time information from a time point when the matching model is generated to a time point when a real matching is accomplished and detecting a matching time for the new matching model based on the current performance tempo.
 10. The score tracking method of claim 1, when the matching model does not match the current performance frequency data until a matching time for the matching model lapses, further comprising a ninth step of adding a new matching model that includes a frequency model, which is included in the frequency model list and is expected to be performed at a next time point after performance of an expected notes frequency model.
 11. The score tracking method of claim 10, wherein the ninth step comprises the steps of: (9-1) generating a new passed notes frequency model using a passed notes frequency model that is included in the matching model; (9-2) generating a new current notes frequency model using a current notes frequency model that is included in the matching model; (9-3) selecting a frequency model, which is expected to be performed at the next time point, from the frequency model list and generating the selected frequency model as a new expected notes frequency model; (9-4) generating a new matching model by combining the new passed notes frequency model, the new current notes frequency model, and the new expected notes frequency model; and (9-5) detecting a matching time for the new matching model based on tempo information with respect to the existing matching model.
 12. The score tracking method of claim 11, wherein the ninth step ends without adding a new matching model when the number of matching models that have not been matched since being generated exceeds a predetermined limit value.
 13. A computer-readable recording medium in which control commands for executing a method for tracking a score is recorded so that the method can be executed when the computer-readable recording medium is driven by a computer, the method comprising: detecting a list of frequency models, which is expected to occur during performance of a score, from score information; generating a matching model, which is composed of frequency components that are expected to be included in audio data externally input at the time, based on the frequency model list; receiving external audio data, converting the audio data into digital signal, and detecting current performance frequency data from the digital signal; determining whether a new frequency component is included in the current performance frequency data; determining whether the matching model matches the current performance frequency data when a new frequency component is included in the current performance frequency data; and generating synchronized information between real performing notes and score notes and updating the matching model, when it is determined that the matching model matches the current performance frequency data.
 14. A computer-executable method for automatically tracking a performance location in a score, comprising: a first step of receiving an execution command; a second step of detecting a list of frequency models, which are expected to occur during music performance, from information about a score to be performed, in response to the execution command; a third step of analyzing a frequency component of digital signal, which is performed outside and received through an audio input unit of the computer or which is reproduced in the computer; a fourth step of generating a matching model of a frequency component, which is expected to occur at the current time, during the music performance, based on the frequency model list detected in the second step; and a fifth step of comparing the frequency component that is analyzed in the third step with the matching model generated in the fourth step to track a current performance location.
 15. A score tracking apparatus comprising: a digital signal input unit that receives music performed outside and converts it into digital signal; a frequency analyzer that extracts frequency components from the digital signal; a score information input unit that inputs score information, which comprises pitch and length information of each note included in a score to be performed; a frequency modeling unit that detects frequency models of notes, which are to be simultaneously performed at each time point when a new note is expected to be performed, by performing frequency modeling on the score information, and generates a list of the frequency models; a storage unit that stores and manages the generated frequency model list; a performance location determiner that receives a frequency component of currently performed digital signal from the frequency analyzer and performs a matching operation on the received frequency component and the frequency model list stored in the storage unit so as to determine a current performance location; and a determination result output unit that provides the result of the performance location determiner to a user.
 16. The score tracking apparatus of claim 15, wherein the performance location determiner generates a matching model of a frequency component, which is expected to occur at the current time, during performance, based on the frequency model list stored in the storage unit; and performs the matching operation on a frequency component of currently performed digital signal, which is received through the frequency analyser, and the matching model.
 17. The score tracking apparatus of claim 14 or 15, wherein the performance location determiner detects a frequency model of a note performed at the previous time, a frequency model of a note performed at the current time, and a frequency model of a note expected to be performed at the next time, and generates a matching model by combining these frequency models. 